HumanHeartProject - QC and UMAPS
I. Hanka
2025-09-29
load packages
load object
fileNam <- "/Users/immbio/Desktop/HumanHeartCarTrans2/data/Human_heart_allmerged_seurat.rds"
seuratM <- readRDS(fileNam)
table(seuratM$dataset)##
## 331571_3-5_20231012_Hu_nucseq_USZ_HTx001 331571_4-6_20231012_Hu_nucseq_USZ_HTx002
## 3400 2539
## 334131_01-1_20231103_Hu_nucseq_USZ_HTx002_RV 334131_02-2_20231103_Hu_nucseq_USZ_HTx003_LV
## 4528 4179
## 334131_03-3_20231103_Hu_nucseq_USZ_HTx004_RV 334131_04-4_20231106_Hu_nucseq_USZ_HTx003_RV
## 4217 3958
## 334131_05-5_20231106_Hu_nucseq_USZ_HTx004_LV 334131_06-6_20231106_Hu_nucseq_USZ_HTx005_RV
## 5294 2917
## 334131_07-7_20231107_Hu_nucseq_USZ_HTx005_LV 334131_08-8_20231107_Hu_nucseq_USZ_HTx006_RV
## 1894 2566
## 334131_09-9_20231108_Hu_nucseq_USZ_HTx006_LV 334131_10-10_20231108_Hu_nucseq_USZ_HTx001_RV
## 3532 2129
## 336431_07-7_20231129_Hu_nucseq_USZ_HTx008_RV 336431_08-8_20231129_Hu_nucseq_USZ_EMB001_V1
## 5295 509
## 336431_09-9_20231129_Hu_nucseq_USZ_HTx008_LV 336431_10-10_20231129_Hu_nucseq_USZ_EMB005_V1
## 3936 1132
## 336431_11-11_20231129_Hu_nucseq_USZ_EMB006_V1 336431_12-12_20231129_Hu_nucseq_USZ_EMB007_V1
## 493 1524
## 336431_13-13_20231129_Hu_nucseq_USZ_EMB002_V1 336431_14-14_20231129_Hu_nucseq_USZ_EMB003_V1
## 1941 1987
## 336431_15-15_20231129_Hu_nucseq_USZ_EMB004_V1 340821_01-7_20240118_Hu_nucseq_USZ_EMB007_V2
## 1688 1838
## 340821_02-8_20240118_Hu_nucseq_USZ_EMB008_V2 340821_03-9_20240123_Hu_nucseq_USZ_EMB001_V3
## 589 71
## 340821_04-10_20240123_Hu_nucseq_USZ_EMB002_V3 340821_05-11_20240123_Hu_nucseq_USZ_EMB003_V3
## 368 175
## 340821_06-12_20240123_Hu_nucseq_USZ_EMB004_V3 340821_07-13_20240123_Hu_nucseq_USZ_EMB005_V3
## 857 2057
## 340821_08-14_20240123_Hu_nucseq_USZ_EMB006_V3 340821_09-15_20240123_Hu_nucseq_USZ_EMB007_V3
## 258 403
## 340821_10-16_20240123_Hu_nucseq_USZ_EMB008_V3 340821_11-17_20240123_Hu_nucseq_USZ_EMB008_V1
## 193 55
## 340821_12-18_20240123_Hu_nucseq_USZ_HTx007_RV 340821_13-19_20240123_Hu_nucseq_USZ_HTx007_LV
## 2800 2581
## 340831_1-1_20240118_Hu_nucseq_USZ_EMB001_V2 340831_2-2_20240118_Hu_nucseq_USZ_EMB002_V2
## 1184 2976
## 340831_3-3_20240118_Hu_nucseq_USZ_EMB003_V2 340831_4-4_20240118_Hu_nucseq_USZ_EMB004_V2
## 418 638
## 340831_5-5_20240118_Hu_nucseq_USZ_EMB005_V2 340831_6-6_20240118_Hu_nucseq_USZ_EMB006_V2
## 719 863
## 347741_1-1_20240326_Hu_nucseq_USZ_HTx009_RV 347741_2-2_20240326_Hu_nucseq_USZ_HTx009_LV
## 3880 6662
## 347741_4-4_20240326_Hu_nucseq_USZ_HTx010_RV 347741_5-5_20240326_Hu_nucseq_USZ_HTx010_LV
## 2231 3143
## 353921_01-1_20240515_Hu_nucseq_USZ_HTx011_RV 353921_02-2_20240515_Hu_nucseq_USZ_HTx011_LV
## 1954 1433
## 353921_03-3_20240515_Hu_nucseq_USZ_EMB011_V1 353921_04-4_20240515_Hu_nucseq_USZ_EMB011_V2
## 857 245
## 353921_05-5_20240515_Hu_nucseq_USZ_HTx012_RV 353921_06-6_20240515_Hu_nucseq_USZ_HTx012_LV
## 1393 3138
## 353921_07-7_20240515_Hu_nucseq_USZ_EMB012_V1 353921_08-8_20240515_Hu_nucseq_USZ_EMB010_V3
## 826 743
## 353921_09-9_20240515_Hu_nucseq_USZ_HTx013_RV 353921_10-10_20240515_Hu_nucseq_USZ_HTx013_LV
## 1909 2673
## 353921_11-11_20240515_Hu_nucseq_USZ_EMB010_V1_1 353921_12-12_20240515_Hu_nucseq_USZ_EMB010_V1_2
## 1118 292
## 353921_13-13_20240524_Hu_nucseq_USZ_HTx015_RV 353921_14-14_20240524_Hu_nucseq_USZ_EMB011_V3
## 2227 672
## 353921_15-15_20240524_Hu_nucseq_USZ_EMB012_V2 353921_17-17_20240524_Hu_nucseq_USZ_HTx015_LV
## 2740 1491
## 353921_18-18_20240524_Hu_nucseq_USZ_EMB013_V1 353921_19-19_20240524_Hu_nucseq_USZ_EMB013_V2
## 541 139
## 353921_20-20_20240524_Hu_nucseq_USZ_EMB013_V3 353921_21-21_20240524_Hu_nucseq_USZ_HTx014_RV
## 401 2538
## 353921_22-22_20240524_Hu_nucseq_USZ_HTx014_LV 353921_23-23_20240524_Hu_nucseq_USZ_EMB014_V1
## 2606 1106
## 353921_24-24_20240524_Hu_nucseq_USZ_EMB014_V2 359861_01-1_20240715_Hu_nucseq_USZ_HTx016_LV
## 360 6256
## 359861_02-2_20240715_Hu_nucseq_USZ_HTx016_RV 359861_03-3_20240715_Hu_nucseq_USZ_EMB014_V3
## 7622 437
## 359861_04-4_20240715_Hu_nucseq_USZ_EMB015_V1 359861_05-5_20240715_Hu_nucseq_USZ_EMB015_V2
## 178 216
## 359861_06-6_20240715_Hu_nucseq_USZ_EMB015_V3 359861_07-7_20240715_Hu_nucseq_USZ_EMB016_V1
## 477 607
## 359861_08-8_20240715_Hu_nucseq_USZ_EMB016_V2 359861_18-10_20240730_Hu_nucseq_USZ_EMB016_V3
## 107 295
## 372561_08-8_20241204_Hu_nucseq_USZ_HTx004_VX1 372561_09-9_20241204_Hu_nucseq_USZ_HTx004_VX2
## 1044 453
## 372561_10-10_20241204_Hu_nucseq_USZ_HTx007_V4 372561_11-11_20241204_Hu_nucseq_USZ_HTx007_VX1
## 431 432
## 372561_12-12_20241204_Hu_nucseq_USZ_HTx007_VX2 372561_13-13_20241204_Hu_nucseq_USZ_HTx007_V5
## 1166 104
## 373291_09-1_20241212_Hu_nucseq_USZ_HTx006_V4 373291_10-2_20241212_Hu_nucseq_USZ_HTx006_V5
## 321 114
## 373291_11-3_20241212_Hu_nucseq_USZ_HTx006_VX1 373291_12-4_20241212_Hu_nucseq_USZ_HTx006_VX2
## 340 366
## 373291_13-5_20241212_Hu_nucseq_USZ_HTx006_VX3 373291_14-6_20241212_Hu_nucseq_USZ_HTx018_expRV
## 62 570
## 373291_15-7_20241212_Hu_nucseq_USZ_HTx018_expLV 373291_16-8_20241212_Hu_nucseq_USZ_HTx018_V1
## 445 60
## 373291_17-9_20241212_Hu_nucseq_USZ_HTx018_V2 373291_18-10_20241212_Hu_nucseq_USZ_HTx018_V3
## 382 42
## 373291_19-11_20241212_Hu_nucseq_USZ_HTx018_V4 373291_20-12_20241212_Hu_nucseq_USZ_HTx018_VX1
## 430 199
## 373291_21-13_20241212_Hu_nucseq_USZ_HTx004_V4 373291_22-14_20241212_Hu_nucseq_USZ_HTx004_V5
## 4026 294
## 377461_2-2_20250212_Hu_nucseq_USZ_HTx004_VX3 384091_01-1_20250423_Hu_nucseq_USZ_HTx019_V1
## 480 706
## 384091_02-2_20250423_Hu_nucseq_USZ_HTx019_V2 384091_03-3_20250423_Hu_nucseq_USZ_HTx019_V3
## 907 472
## 384091_04-4_20250423_Hu_nucseq_USZ_HTx019_VX1 384091_05-5_20250423_Hu_nucseq_USZ_HTx002_VX2
## 408 642
## 384091_06-6_20250423_Hu_nucseq_USZ_HTx002_VX3 384091_07-7_20250423_Hu_nucseq_USZ_HTx007_VX3
## 737 186
## 384091_08-8_20250423_Hu_nucseq_USZ_HTx007_VX4 384091_09-9_20250423_Hu_nucseq_USZ_HTx008_VX2
## 402 64
## 384091_10-10_20250423_Hu_nucseq_USZ_HTx008_VX3 384091_11-11_20250423_Hu_nucseq_USZ_HTx018_VX2
## 542 93
## o28576_1_08-8_20220525_Hu_nucseq_Graz_8_HH_GEM o28576_1_10-10_20220525_Hu_nucseq_Graz_10_HH_GEM
## 4005 3922
## o28576_1_11-11_20220525_Hu_nucseq_Graz_11_HH_GEM o28576_1_12-12_20220525_Hu_nucseq_Graz_12_HH_GEM
## 4265 3853
## o292731_1-1_20220818_Hu_nucseq_Graz_9_HH_GEM o292731_2-2_20220818_Hu_nucseq_Graz_13_HH_GEM
## 6434 11568
## o294781_01-1_20220912_Hu_nucseq_Graz_21_HH_GEM o294781_02-2_20220912_Hu_nucseq_Graz_22_HH_GEM
## 1465 2064
## o294781_03-3_20220912_Hu_nucseq_Graz_23_HH_GEM o294781_04-4_20220912_Hu_nucseq_Graz_24_HH_GEM
## 866 2181table(seuratM$RNA_snn_res.0.25)##
## 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
## 59672 32420 28550 25377 17125 12696 4416 3906 3653 3212 2569 2159 1107 1091 1004 800table(seuratM$orig.ident)##
##
## 199757add metadata
# add any type of metadata
## patient
seuratM$patient <- "pat_nr"
seuratM$patient[grepl("HTx001|EMB001", seuratM$dataset)] <- "CarTransPat01"
seuratM$patient[grepl("HTx002|EMB002", seuratM$dataset)] <- "CarTransPat02"
seuratM$patient[grepl("HTx003|EMB003", seuratM$dataset)] <- "CarTransPat03"
seuratM$patient[grepl("HTx004|EMB004", seuratM$dataset)] <- "CarTransPat04"
seuratM$patient[grepl("HTx005|EMB005", seuratM$dataset)] <- "CarTransPat05"
seuratM$patient[grepl("HTx006|EMB006", seuratM$dataset)] <- "CarTransPat06"
seuratM$patient[grepl("HTx007|EMB007", seuratM$dataset)] <- "CarTransPat07"
seuratM$patient[grepl("HTx008|EMB008", seuratM$dataset)] <- "CarTransPat08"
seuratM$patient[grepl("HTx009|EMB009", seuratM$dataset)] <- "CarTransPat09"
seuratM$patient[grepl("HTx010|EMB010", seuratM$dataset)] <- "CarTransPat10"
seuratM$patient[grepl("HTx011|EMB011", seuratM$dataset)] <- "CarTransPat11"
seuratM$patient[grepl("HTx012|EMB012", seuratM$dataset)] <- "CarTransPat12"
seuratM$patient[grepl("HTx013|EMB013", seuratM$dataset)] <- "CarTransPat13"
seuratM$patient[grepl("HTx014|EMB014", seuratM$dataset)] <- "CarTransPat14"
seuratM$patient[grepl("HTx015|EMB015", seuratM$dataset)] <- "CarTransPat15"
seuratM$patient[grepl("HTx016|EMB016", seuratM$dataset)] <- "CarTransPat16"
seuratM$patient[grepl("HTx018|EMB018", seuratM$dataset)] <- "CarTransPat18"
seuratM$patient[grepl("HTx019|EMB019", seuratM$dataset)] <- "CarTransPat19"
seuratM$patient[grepl("HTx024|EMB024", seuratM$dataset)] <- "CarTransPat24"
seuratM$patient[which(seuratM$dataset == "o28576_1_08-8_20220525_Hu_nucseq_Graz_8_HH_GEM")] <- "DH01"
seuratM$patient[which(seuratM$dataset == "o28576_1_10-10_20220525_Hu_nucseq_Graz_10_HH_GEM")] <- "DH02"
seuratM$patient[which(seuratM$dataset == "o28576_1_11-11_20220525_Hu_nucseq_Graz_11_HH_GEM")] <- "DH03"
seuratM$patient[which(seuratM$dataset == "o28576_1_12-12_20220525_Hu_nucseq_Graz_12_HH_GEM")] <- "DH04"
seuratM$patient[which(seuratM$dataset =="o292731_1-1_20220818_Hu_nucseq_Graz_9_HH_GEM")] <- "DH05"
seuratM$patient[which(seuratM$dataset =="o292731_2-2_20220818_Hu_nucseq_Graz_13_HH_GEM")] <- "DH06"
seuratM$patient[which(seuratM$dataset == "o294781_01-1_20220912_Hu_nucseq_Graz_21_HH_GEM")] <- "DH07"
seuratM$patient[which(seuratM$dataset == "o294781_02-2_20220912_Hu_nucseq_Graz_22_HH_GEM")] <- "DH08"
seuratM$patient[which(seuratM$dataset == "o294781_03-3_20220912_Hu_nucseq_Graz_23_HH_GEM")] <- "DH09"
seuratM$patient[which(seuratM$dataset == "o294781_04-4_20220912_Hu_nucseq_Graz_24_HH_GEM")] <- "DH10"
table(seuratM$patient)##
## CarTransPat01 CarTransPat02 CarTransPat03 CarTransPat04 CarTransPat05 CarTransPat06 CarTransPat07
## 7293 13731 10717 18991 8719 8915 11867
## CarTransPat08 CarTransPat09 CarTransPat10 CarTransPat11 CarTransPat12 CarTransPat13 CarTransPat14
## 10674 10542 7527 5161 8097 5663 7047
## CarTransPat15 CarTransPat16 CarTransPat18 CarTransPat19 DH01 DH02 DH03
## 4589 14887 2221 2493 4005 3922 4265
## DH04 DH05 DH06 DH07 DH08 DH09 DH10
## 3853 6434 11568 1465 2064 866 2181ordpatients <- c("DH01", "DH02", "DH03", "DH04", "DH05", "DH06", "DH07", "DH08", "DH09", "DH10", "CarTransPat01", "CarTransPat02", "CarTransPat03", "CarTransPat04", "CarTransPat05", "CarTransPat06", "CarTransPat07", "CarTransPat08", "CarTransPat09", "CarTransPat10", "CarTransPat11", "CarTransPat12", "CarTransPat13", "CarTransPat14", "CarTransPat15", "CarTransPat16", "CarTransPat18", "CarTransPat19", "CarTransPat24")
Idents(seuratM) <- seuratM$patient
seuratM$patient <- factor(seuratM$patient, levels=ordpatients)
Idents(seuratM) <- seuratM$patient
table(seuratM$patient)##
## DH01 DH02 DH03 DH04 DH05 DH06 DH07
## 4005 3922 4265 3853 6434 11568 1465
## DH08 DH09 DH10 CarTransPat01 CarTransPat02 CarTransPat03 CarTransPat04
## 2064 866 2181 7293 13731 10717 18991
## CarTransPat05 CarTransPat06 CarTransPat07 CarTransPat08 CarTransPat09 CarTransPat10 CarTransPat11
## 8719 8915 11867 10674 10542 7527 5161
## CarTransPat12 CarTransPat13 CarTransPat14 CarTransPat15 CarTransPat16 CarTransPat18 CarTransPat19
## 8097 5663 7047 4589 14887 2221 2493
## CarTransPat24
## 0### note visit3 of CarTransPat12 is missing - sample/data quality not sufficient
### diseaseCond
seuratM$diseaseCond <- "diseaseCond"
seuratM$diseaseCond[grepl("V1", seuratM$dataset)] <- "visit1"
seuratM$diseaseCond[grepl("V2|353921_12-12_20240515_Hu_nucseq_USZ_EMB010_V1_2", seuratM$dataset)] <- "visit2"
seuratM$diseaseCond[grepl("V3", seuratM$dataset)] <- "visit3"
seuratM$diseaseCond[grepl("V4", seuratM$dataset)] <- "visit4"
seuratM$diseaseCond[grepl("V5", seuratM$dataset)] <- "visit5"
seuratM$diseaseCond[grepl("VX1", seuratM$dataset)] <- "visitX1"
seuratM$diseaseCond[grepl("VX2", seuratM$dataset)] <- "visitX2"
seuratM$diseaseCond[grepl("VX3", seuratM$dataset)] <- "visitX3"
seuratM$diseaseCond[grepl("VX4", seuratM$dataset)] <- "visitX4"
seuratM$diseaseCond[grepl("HH", seuratM$dataset)] <- "donorheart"
seuratM$diseaseCond[grepl("RV|LV|expLV|expRV|331571_3-5_20231012_Hu_nucseq_USZ_HTx001|331571_4-6_20231012_Hu_nucseq_USZ_HTx002", seuratM$dataset)] <- "explant"
table(seuratM$diseaseCond)##
## donorheart explant visit1 visit2 visit3 visit4 visit5 visitX1 visitX2
## 40623 107936 15328 14613 7921 5208 512 2423 2784
## visitX3 visitX4
## 2007 402orddiseaseCond <- c("donorheart","visit1", "visit2" ,"visit3", "visit4", "visit5", "visitX1", "visitX2", "visitX3", "visitX4", "explant")
Idents(seuratM) <- seuratM$diseaseCond
seuratM$diseaseCond <- factor(seuratM$diseaseCond, levels=orddiseaseCond)
Idents(seuratM) <- seuratM$diseaseCond
table(seuratM$diseaseCond)##
## donorheart visit1 visit2 visit3 visit4 visit5 visitX1 visitX2 visitX3
## 40623 15328 14613 7921 5208 512 2423 2784 2007
## visitX4 explant
## 402 107936#### cluster_name
seuratM$clusterName <- "clusterName"
seuratM$clusterName[which(seuratM$RNA_snn_res.0.4 %in% "0" )] <- "Fb1"
seuratM$clusterName[which(seuratM$RNA_snn_res.0.4 %in% "1" )] <- "PerivFb1"
seuratM$clusterName[which(seuratM$RNA_snn_res.0.4 %in% "2" )] <- "Mph2"
seuratM$clusterName[which(seuratM$RNA_snn_res.0.4 %in% "3" )] <- "BEC1"
seuratM$clusterName[which(seuratM$RNA_snn_res.0.4 %in% "4" )] <- "Fb2"
seuratM$clusterName[which(seuratM$RNA_snn_res.0.4 %in% "5" )] <- "CM"
seuratM$clusterName[which(seuratM$RNA_snn_res.0.4 %in% "6" )] <- "Tcell1"
seuratM$clusterName[which(seuratM$RNA_snn_res.0.4 %in% "7" )] <- "BEC2"
seuratM$clusterName[which(seuratM$RNA_snn_res.0.4 %in% "8" )] <- "VSMC"
seuratM$clusterName[which(seuratM$RNA_snn_res.0.4 %in% "9" )] <- "Mph1"
seuratM$clusterName[which(seuratM$RNA_snn_res.0.4 %in% "10" )] <- "BEC3"
seuratM$clusterName[which(seuratM$RNA_snn_res.0.4 %in% "11" )] <- "NC"
seuratM$clusterName[which(seuratM$RNA_snn_res.0.4 %in% "12" )] <- "BaroRec"
seuratM$clusterName[which(seuratM$RNA_snn_res.0.4 %in% "13" )] <- "Bcell"
seuratM$clusterName[which(seuratM$RNA_snn_res.0.4 %in% "14" )] <- "Fb3"
seuratM$clusterName[which(seuratM$RNA_snn_res.0.4 %in% "15" )] <- "Tcell2"
seuratM$clusterName[which(seuratM$RNA_snn_res.0.4 %in% "16" )] <- "LEC"
seuratM$clusterName[which(seuratM$RNA_snn_res.0.4 %in% "17" )] <- "PerivFb2"
seuratM$clusterName[which(seuratM$RNA_snn_res.0.4 %in% "18" )] <- "Adipoc"
table(seuratM$clusterName)##
## Adipoc BaroRec Bcell BEC1 BEC2 BEC3 CM Fb1 Fb2 Fb3 LEC
## 800 2447 2308 25088 7101 3884 17271 39801 19725 2195 1092
## Mph1 Mph2 NC PerivFb1 PerivFb2 Tcell1 Tcell2 VSMC
## 3903 25131 2566 27466 999 12718 1156 4106table(seuratM$RNA_snn_res.0.4)##
## 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
## 39801 27466 25131 25088 19725 17271 12718 7101 4106 3903 3884 2566 2447 2308 2195 1156
## 16 17 18
## 1092 999 800###order
Idents(seuratM) <- seuratM$clusterName
seuratM$clusterName <- factor(seuratM$clusterName, levels=c("CM","Fb1","Fb2","Fb3","PerivFb1","PerivFb2","VSMC","BEC1","BEC2","BEC3","LEC","NC","BaroRec","Adipoc","Mph1","Mph2","Tcell1","Tcell2","Bcell"))
Idents(seuratM) <- seuratM$clusterName
table(seuratM$clusterName)##
## CM Fb1 Fb2 Fb3 PerivFb1 PerivFb2 VSMC BEC1 BEC2 BEC3 LEC
## 17271 39801 19725 2195 27466 999 4106 25088 7101 3884 1092
## NC BaroRec Adipoc Mph1 Mph2 Tcell1 Tcell2 Bcell
## 2566 2447 800 3903 25131 12718 1156 2308###combined slots
seuratM$patient_diseaseCond <- paste0(seuratM$patient, '_', seuratM$diseaseCond)
table(seuratM$patient_diseaseCond)##
## CarTransPat01_explant CarTransPat01_visit1 CarTransPat01_visit2 CarTransPat01_visit3
## 5529 509 1184 71
## CarTransPat02_explant CarTransPat02_visit1 CarTransPat02_visit2 CarTransPat02_visit3
## 7067 1941 2976 368
## CarTransPat02_visitX2 CarTransPat02_visitX3 CarTransPat03_explant CarTransPat03_visit1
## 642 737 8137 1987
## CarTransPat03_visit2 CarTransPat03_visit3 CarTransPat04_explant CarTransPat04_visit1
## 418 175 9511 1688
## CarTransPat04_visit2 CarTransPat04_visit3 CarTransPat04_visit4 CarTransPat04_visit5
## 638 857 4026 294
## CarTransPat04_visitX1 CarTransPat04_visitX2 CarTransPat04_visitX3 CarTransPat05_explant
## 1044 453 480 4811
## CarTransPat05_visit1 CarTransPat05_visit2 CarTransPat05_visit3 CarTransPat06_explant
## 1132 719 2057 6098
## CarTransPat06_visit1 CarTransPat06_visit2 CarTransPat06_visit3 CarTransPat06_visit4
## 493 863 258 321
## CarTransPat06_visit5 CarTransPat06_visitX1 CarTransPat06_visitX2 CarTransPat06_visitX3
## 114 340 366 62
## CarTransPat07_explant CarTransPat07_visit1 CarTransPat07_visit2 CarTransPat07_visit3
## 5381 1524 1838 403
## CarTransPat07_visit4 CarTransPat07_visit5 CarTransPat07_visitX1 CarTransPat07_visitX2
## 431 104 432 1166
## CarTransPat07_visitX3 CarTransPat07_visitX4 CarTransPat08_explant CarTransPat08_visit1
## 186 402 9231 55
## CarTransPat08_visit2 CarTransPat08_visit3 CarTransPat08_visitX2 CarTransPat08_visitX3
## 589 193 64 542
## CarTransPat09_explant CarTransPat10_explant CarTransPat10_visit1 CarTransPat10_visit2
## 10542 5374 1118 292
## CarTransPat10_visit3 CarTransPat11_explant CarTransPat11_visit1 CarTransPat11_visit2
## 743 3387 857 245
## CarTransPat11_visit3 CarTransPat12_explant CarTransPat12_visit1 CarTransPat12_visit2
## 672 4531 826 2740
## CarTransPat13_explant CarTransPat13_visit1 CarTransPat13_visit2 CarTransPat13_visit3
## 4582 541 139 401
## CarTransPat14_explant CarTransPat14_visit1 CarTransPat14_visit2 CarTransPat14_visit3
## 5144 1106 360 437
## CarTransPat15_explant CarTransPat15_visit1 CarTransPat15_visit2 CarTransPat15_visit3
## 3718 178 216 477
## CarTransPat16_explant CarTransPat16_visit1 CarTransPat16_visit2 CarTransPat16_visit3
## 13878 607 107 295
## CarTransPat18_explant CarTransPat18_visit1 CarTransPat18_visit2 CarTransPat18_visit3
## 1015 60 382 42
## CarTransPat18_visit4 CarTransPat18_visitX1 CarTransPat18_visitX2 CarTransPat19_visit1
## 430 199 93 706
## CarTransPat19_visit2 CarTransPat19_visit3 CarTransPat19_visitX1 DH01_donorheart
## 907 472 408 4005
## DH02_donorheart DH03_donorheart DH04_donorheart DH05_donorheart
## 3922 4265 3853 6434
## DH06_donorheart DH07_donorheart DH08_donorheart DH09_donorheart
## 11568 1465 2064 866
## DH10_donorheart
## 2181seuratM$patient_clusterName <- paste0(seuratM$patient, '_', seuratM$clusterName)
table(seuratM$patient_clusterName)##
## CarTransPat01_Adipoc CarTransPat01_BaroRec CarTransPat01_Bcell CarTransPat01_BEC1
## 103 58 73 833
## CarTransPat01_BEC2 CarTransPat01_BEC3 CarTransPat01_CM CarTransPat01_Fb1
## 216 30 321 2720
## CarTransPat01_Fb2 CarTransPat01_Fb3 CarTransPat01_LEC CarTransPat01_Mph1
## 566 107 85 118
## CarTransPat01_Mph2 CarTransPat01_NC CarTransPat01_PerivFb1 CarTransPat01_PerivFb2
## 908 73 552 30
## CarTransPat01_Tcell1 CarTransPat01_Tcell2 CarTransPat01_VSMC CarTransPat02_Adipoc
## 318 46 136 44
## CarTransPat02_BaroRec CarTransPat02_Bcell CarTransPat02_BEC1 CarTransPat02_BEC2
## 93 208 854 284
## CarTransPat02_BEC3 CarTransPat02_CM CarTransPat02_Fb1 CarTransPat02_Fb2
## 361 1382 3717 514
## CarTransPat02_Fb3 CarTransPat02_LEC CarTransPat02_Mph1 CarTransPat02_Mph2
## 108 96 295 1851
## CarTransPat02_NC CarTransPat02_PerivFb1 CarTransPat02_PerivFb2 CarTransPat02_Tcell1
## 221 1357 29 1983
## CarTransPat02_Tcell2 CarTransPat02_VSMC CarTransPat03_Adipoc CarTransPat03_BaroRec
## 143 191 1 418
## CarTransPat03_Bcell CarTransPat03_BEC1 CarTransPat03_BEC2 CarTransPat03_BEC3
## 135 2498 539 216
## CarTransPat03_CM CarTransPat03_Fb1 CarTransPat03_Fb2 CarTransPat03_Fb3
## 436 1354 1413 171
## CarTransPat03_LEC CarTransPat03_Mph1 CarTransPat03_Mph2 CarTransPat03_NC
## 38 149 1218 227
## CarTransPat03_PerivFb1 CarTransPat03_PerivFb2 CarTransPat03_Tcell1 CarTransPat03_Tcell2
## 919 68 683 62
## CarTransPat03_VSMC CarTransPat04_Adipoc CarTransPat04_BaroRec CarTransPat04_Bcell
## 172 29 220 431
## CarTransPat04_BEC1 CarTransPat04_BEC2 CarTransPat04_BEC3 CarTransPat04_CM
## 1669 614 268 1623
## CarTransPat04_Fb1 CarTransPat04_Fb2 CarTransPat04_Fb3 CarTransPat04_LEC
## 2164 2592 194 327
## CarTransPat04_Mph1 CarTransPat04_Mph2 CarTransPat04_NC CarTransPat04_PerivFb1
## 480 3109 127 1698
## CarTransPat04_PerivFb2 CarTransPat04_Tcell1 CarTransPat04_Tcell2 CarTransPat04_VSMC
## 58 2864 179 345
## CarTransPat05_Adipoc CarTransPat05_BaroRec CarTransPat05_Bcell CarTransPat05_BEC1
## 15 43 126 821
## CarTransPat05_BEC2 CarTransPat05_BEC3 CarTransPat05_CM CarTransPat05_Fb1
## 259 151 455 2509
## CarTransPat05_Fb2 CarTransPat05_Fb3 CarTransPat05_LEC CarTransPat05_Mph1
## 549 57 69 151
## CarTransPat05_Mph2 CarTransPat05_NC CarTransPat05_PerivFb1 CarTransPat05_PerivFb2
## 1885 129 817 18
## CarTransPat05_Tcell1 CarTransPat05_Tcell2 CarTransPat05_VSMC CarTransPat06_Adipoc
## 491 25 149 186
## CarTransPat06_BaroRec CarTransPat06_Bcell CarTransPat06_BEC1 CarTransPat06_BEC2
## 124 140 702 180
## CarTransPat06_BEC3 CarTransPat06_CM CarTransPat06_Fb1 CarTransPat06_Fb2
## 223 514 1640 1639
## CarTransPat06_Fb3 CarTransPat06_LEC CarTransPat06_Mph1 CarTransPat06_Mph2
## 60 100 129 1148
## CarTransPat06_NC CarTransPat06_PerivFb1 CarTransPat06_PerivFb2 CarTransPat06_Tcell1
## 94 1221 28 566
## CarTransPat06_Tcell2 CarTransPat06_VSMC CarTransPat07_Adipoc CarTransPat07_BaroRec
## 51 170 13 60
## CarTransPat07_Bcell CarTransPat07_BEC1 CarTransPat07_BEC2 CarTransPat07_BEC3
## 339 1240 374 304
## CarTransPat07_CM CarTransPat07_Fb1 CarTransPat07_Fb2 CarTransPat07_Fb3
## 559 3079 652 133
## CarTransPat07_LEC CarTransPat07_Mph1 CarTransPat07_Mph2 CarTransPat07_NC
## 30 223 1803 196
## CarTransPat07_PerivFb1 CarTransPat07_PerivFb2 CarTransPat07_Tcell1 CarTransPat07_Tcell2
## 1103 50 1429 59
## CarTransPat07_VSMC CarTransPat08_Adipoc CarTransPat08_BaroRec CarTransPat08_Bcell
## 221 12 90 62
## CarTransPat08_BEC1 CarTransPat08_BEC2 CarTransPat08_BEC3 CarTransPat08_CM
## 2008 579 180 458
## CarTransPat08_Fb1 CarTransPat08_Fb2 CarTransPat08_Fb3 CarTransPat08_LEC
## 1555 2350 196 13
## CarTransPat08_Mph1 CarTransPat08_Mph2 CarTransPat08_NC CarTransPat08_PerivFb1
## 173 989 158 1260
## CarTransPat08_PerivFb2 CarTransPat08_Tcell1 CarTransPat08_Tcell2 CarTransPat08_VSMC
## 64 198 21 308
## CarTransPat09_Adipoc CarTransPat09_BaroRec CarTransPat09_Bcell CarTransPat09_BEC1
## 1 93 47 1587
## CarTransPat09_BEC2 CarTransPat09_BEC3 CarTransPat09_CM CarTransPat09_Fb1
## 484 49 1039 638
## CarTransPat09_Fb2 CarTransPat09_Fb3 CarTransPat09_LEC CarTransPat09_Mph1
## 1678 101 6 272
## CarTransPat09_Mph2 CarTransPat09_NC CarTransPat09_PerivFb1 CarTransPat09_PerivFb2
## 1386 36 2251 83
## CarTransPat09_Tcell1 CarTransPat09_Tcell2 CarTransPat09_VSMC CarTransPat10_Adipoc
## 435 72 284 17
## CarTransPat10_BaroRec CarTransPat10_Bcell CarTransPat10_BEC1 CarTransPat10_BEC2
## 79 33 1101 349
## CarTransPat10_BEC3 CarTransPat10_CM CarTransPat10_Fb1 CarTransPat10_Fb2
## 60 762 1367 692
## CarTransPat10_Fb3 CarTransPat10_LEC CarTransPat10_Mph1 CarTransPat10_Mph2
## 103 15 98 546
## CarTransPat10_NC CarTransPat10_PerivFb1 CarTransPat10_PerivFb2 CarTransPat10_Tcell1
## 133 1320 47 555
## CarTransPat10_Tcell2 CarTransPat10_VSMC CarTransPat11_Adipoc CarTransPat11_BaroRec
## 79 171 28 61
## CarTransPat11_Bcell CarTransPat11_BEC1 CarTransPat11_BEC2 CarTransPat11_BEC3
## 39 466 177 186
## CarTransPat11_CM CarTransPat11_Fb1 CarTransPat11_Fb2 CarTransPat11_Fb3
## 790 986 649 46
## CarTransPat11_LEC CarTransPat11_Mph1 CarTransPat11_Mph2 CarTransPat11_NC
## 47 99 716 55
## CarTransPat11_PerivFb1 CarTransPat11_PerivFb2 CarTransPat11_Tcell1 CarTransPat11_Tcell2
## 475 10 213 23
## CarTransPat11_VSMC CarTransPat12_BaroRec CarTransPat12_Bcell CarTransPat12_BEC1
## 95 76 189 1688
## CarTransPat12_BEC2 CarTransPat12_BEC3 CarTransPat12_CM CarTransPat12_Fb1
## 409 126 641 1108
## CarTransPat12_Fb2 CarTransPat12_Fb3 CarTransPat12_LEC CarTransPat12_Mph1
## 1095 126 10 193
## CarTransPat12_Mph2 CarTransPat12_NC CarTransPat12_PerivFb1 CarTransPat12_PerivFb2
## 958 79 782 49
## CarTransPat12_Tcell1 CarTransPat12_Tcell2 CarTransPat12_VSMC CarTransPat13_Adipoc
## 343 48 177 30
## CarTransPat13_BaroRec CarTransPat13_Bcell CarTransPat13_BEC1 CarTransPat13_BEC2
## 67 113 1178 203
## CarTransPat13_BEC3 CarTransPat13_CM CarTransPat13_Fb1 CarTransPat13_Fb2
## 95 748 327 533
## CarTransPat13_Fb3 CarTransPat13_LEC CarTransPat13_Mph1 CarTransPat13_Mph2
## 101 20 165 737
## CarTransPat13_NC CarTransPat13_PerivFb1 CarTransPat13_PerivFb2 CarTransPat13_Tcell1
## 75 671 55 331
## CarTransPat13_Tcell2 CarTransPat13_VSMC CarTransPat14_Adipoc CarTransPat14_BaroRec
## 34 180 85 56
## CarTransPat14_Bcell CarTransPat14_BEC1 CarTransPat14_BEC2 CarTransPat14_BEC3
## 51 625 211 161
## CarTransPat14_CM CarTransPat14_Fb1 CarTransPat14_Fb2 CarTransPat14_Fb3
## 269 2112 1145 91
## CarTransPat14_LEC CarTransPat14_Mph1 CarTransPat14_Mph2 CarTransPat14_NC
## 36 153 639 74
## CarTransPat14_PerivFb1 CarTransPat14_PerivFb2 CarTransPat14_Tcell1 CarTransPat14_Tcell2
## 826 35 333 59
## CarTransPat14_VSMC CarTransPat15_Adipoc CarTransPat15_BaroRec CarTransPat15_Bcell
## 86 22 76 47
## CarTransPat15_BEC1 CarTransPat15_BEC2 CarTransPat15_BEC3 CarTransPat15_CM
## 237 74 98 480
## CarTransPat15_Fb1 CarTransPat15_Fb2 CarTransPat15_Fb3 CarTransPat15_LEC
## 1643 248 18 37
## CarTransPat15_Mph1 CarTransPat15_Mph2 CarTransPat15_NC CarTransPat15_PerivFb1
## 105 709 57 497
## CarTransPat15_PerivFb2 CarTransPat15_Tcell1 CarTransPat15_Tcell2 CarTransPat15_VSMC
## 6 157 26 52
## CarTransPat16_Adipoc CarTransPat16_BaroRec CarTransPat16_Bcell CarTransPat16_BEC1
## 116 495 80 1157
## CarTransPat16_BEC2 CarTransPat16_BEC3 CarTransPat16_CM CarTransPat16_Fb1
## 266 286 367 4103
## CarTransPat16_Fb2 CarTransPat16_Fb3 CarTransPat16_LEC CarTransPat16_Mph1
## 2314 256 44 531
## CarTransPat16_Mph2 CarTransPat16_NC CarTransPat16_PerivFb1 CarTransPat16_PerivFb2
## 1541 190 2519 91
## CarTransPat16_Tcell1 CarTransPat16_Tcell2 CarTransPat16_VSMC CarTransPat18_Adipoc
## 226 107 198 19
## CarTransPat18_BaroRec CarTransPat18_Bcell CarTransPat18_BEC1 CarTransPat18_BEC2
## 49 81 397 96
## CarTransPat18_BEC3 CarTransPat18_CM CarTransPat18_Fb1 CarTransPat18_Fb2
## 49 132 293 124
## CarTransPat18_Fb3 CarTransPat18_LEC CarTransPat18_Mph1 CarTransPat18_Mph2
## 11 19 17 337
## CarTransPat18_NC CarTransPat18_PerivFb1 CarTransPat18_PerivFb2 CarTransPat18_Tcell1
## 43 208 4 299
## CarTransPat18_Tcell2 CarTransPat18_VSMC CarTransPat19_Adipoc CarTransPat19_BaroRec
## 5 38 1 19
## CarTransPat19_Bcell CarTransPat19_BEC1 CarTransPat19_BEC2 CarTransPat19_BEC3
## 26 193 44 78
## CarTransPat19_CM CarTransPat19_Fb1 CarTransPat19_Fb2 CarTransPat19_Fb3
## 166 273 60 20
## CarTransPat19_LEC CarTransPat19_Mph1 CarTransPat19_Mph2 CarTransPat19_NC
## 3 90 554 20
## CarTransPat19_PerivFb1 CarTransPat19_PerivFb2 CarTransPat19_Tcell1 CarTransPat19_Tcell2
## 139 1 744 47
## CarTransPat19_VSMC DH01_Adipoc DH01_BaroRec DH01_Bcell
## 15 4 26 7
## DH01_BEC1 DH01_BEC2 DH01_BEC3 DH01_CM
## 601 191 17 726
## DH01_Fb1 DH01_Fb2 DH01_Fb3 DH01_LEC
## 1039 106 21 13
## DH01_Mph1 DH01_Mph2 DH01_NC DH01_PerivFb1
## 39 328 72 571
## DH01_PerivFb2 DH01_Tcell1 DH01_Tcell2 DH01_VSMC
## 18 62 8 156
## DH02_Adipoc DH02_BaroRec DH02_Bcell DH02_BEC1
## 66 18 5 316
## DH02_BEC2 DH02_BEC3 DH02_CM DH02_Fb1
## 88 24 1125 1009
## DH02_Fb2 DH02_Fb3 DH02_LEC DH02_Mph1
## 13 17 8 42
## DH02_Mph2 DH02_NC DH02_PerivFb1 DH02_PerivFb2
## 448 36 526 12
## DH02_Tcell1 DH02_Tcell2 DH02_VSMC DH03_BaroRec
## 99 6 64 27
## DH03_Bcell DH03_BEC1 DH03_BEC2 DH03_BEC3
## 20 570 213 30
## DH03_CM DH03_Fb1 DH03_Fb2 DH03_Fb3
## 457 516 304 29
## DH03_LEC DH03_Mph1 DH03_Mph2 DH03_NC
## 1 60 625 30
## DH03_PerivFb1 DH03_PerivFb2 DH03_Tcell1 DH03_Tcell2
## 1170 30 96 13
## DH03_VSMC DH04_BaroRec DH04_Bcell DH04_BEC1
## 74 23 5 933
## DH04_BEC2 DH04_BEC3 DH04_CM DH04_Fb1
## 227 13 118 1070
## DH04_Fb2 DH04_Fb3 DH04_LEC DH04_Mph1
## 38 47 6 19
## DH04_Mph2 DH04_NC DH04_PerivFb1 DH04_PerivFb2
## 202 22 974 34
## DH04_Tcell1 DH04_Tcell2 DH04_VSMC DH05_Adipoc
## 29 2 91 4
## DH05_BaroRec DH05_Bcell DH05_BEC1 DH05_BEC2
## 42 10 612 217
## DH05_BEC3 DH05_CM DH05_Fb1 DH05_Fb2
## 613 854 1534 184
## DH05_Fb3 DH05_LEC DH05_Mph1 DH05_Mph2
## 20 28 67 842
## DH05_NC DH05_PerivFb1 DH05_PerivFb2 DH05_Tcell1
## 73 1052 15 106
## DH05_Tcell2 DH05_VSMC DH06_Adipoc DH06_BaroRec
## 5 156 3 112
## DH06_Bcell DH06_BEC1 DH06_BEC2 DH06_BEC3
## 10 1791 591 251
## DH06_CM DH06_Fb1 DH06_Fb2 DH06_Fb3
## 2318 2262 176 146
## DH06_LEC DH06_Mph1 DH06_Mph2 DH06_NC
## 21 175 814 268
## DH06_PerivFb1 DH06_PerivFb2 DH06_Tcell1 DH06_Tcell2
## 2053 126 77 26
## DH06_VSMC DH07_BaroRec DH07_Bcell DH07_BEC1
## 348 5 10 206
## DH07_BEC2 DH07_CM DH07_Fb1 DH07_Fb2
## 53 78 71 17
## DH07_Fb3 DH07_Mph1 DH07_Mph2 DH07_NC
## 2 15 166 15
## DH07_PerivFb1 DH07_PerivFb2 DH07_Tcell1 DH07_Tcell2
## 737 11 9 7
## DH07_VSMC DH08_BaroRec DH08_Bcell DH08_BEC1
## 63 7 11 229
## DH08_BEC2 DH08_BEC3 DH08_CM DH08_Fb1
## 40 2 221 303
## DH08_Fb2 DH08_Fb3 DH08_Mph1 DH08_Mph2
## 18 8 26 407
## DH08_NC DH08_PerivFb1 DH08_PerivFb2 DH08_Tcell1
## 32 663 8 27
## DH08_Tcell2 DH08_VSMC DH09_BaroRec DH09_Bcell
## 1 61 5 4
## DH09_BEC1 DH09_BEC2 DH09_BEC3 DH09_CM
## 167 51 8 62
## DH09_Fb1 DH09_Fb2 DH09_Fb3 DH09_LEC
## 42 27 3 7
## DH09_Mph1 DH09_Mph2 DH09_NC DH09_PerivFb1
## 9 123 8 307
## DH09_PerivFb2 DH09_Tcell1 DH09_Tcell2 DH09_VSMC
## 8 8 1 26
## DH10_Adipoc DH10_BaroRec DH10_Bcell DH10_BEC1
## 1 5 6 409
## DH10_BEC2 DH10_BEC3 DH10_CM DH10_Fb1
## 72 5 170 367
## DH10_Fb2 DH10_Fb3 DH10_LEC DH10_Mph1
## 29 3 13 10
## DH10_Mph2 DH10_NC DH10_PerivFb1 DH10_PerivFb2
## 142 23 798 11
## DH10_Tcell1 DH10_Tcell2 DH10_VSMC
## 37 1 79Quality Control analysis: Samples with low nuclei numbers are CarTrans 01 Visit 3: 71 nuclei CarTrans 06 Visit X3: 62 nuclei CarTrans 08 Visit 1: 55 nuclei CarTrans 08 Visit X2: 64 nuclei CarTrans 18 Visit 1: 60 nuclei CarTrans 18 Visit 3: 42 nuclei
##set color vectors
colclusterName <- c("#67001f", "#f4a582","#D53E4F", "#B45B5C","#003c30","#01665e","#66C2A5", "#BEAEF8","#BEAED4", "#c7eae5", "#B09C85", "#4e5a4c","#393A3F","pink","#4588CA","#3299CA","#FCC80B","#FEE60B","#628395")
names(colclusterName) <- c("CM","Fb1","Fb2","Fb3","PerivFb1","PerivFb2","VSMC","BEC1","BEC2","BEC3","LEC","NC","BaroRec","Adipoc","Mph1","Mph2","Tcell1","Tcell2","Bcell")
coldiseaseCond <- c("#dfc27d","#BE3144","#f4a582","#B45B5C","#8c510a","#202547","#355C7D","#779d8d", "#01665e", "#3288BD", "#BEAED4")
names(coldiseaseCond) <- c("donorheart", "explant", "visit1", "visit2", "visit3", "visit4", "visit5", "visitX1", "visitX2", "visitX3", "visitX4")QC merged
#Violin Plot of RNA counts and features
Seurat::VlnPlot(seuratM, features = c ("nCount_RNA", "nFeature_RNA"))
#Mitochondrial Genes
Mito.gene <-Seurat::PercentageFeatureSet(seuratM, pattern="^MT-", col.name = "percent.mito")
Seurat::VlnPlot(Mito.gene, features = ("percent.mito"))
# Extract meta.data from the Seurat object
meta.data <- seuratM@meta.data
# Create the density plot
ptotalpat <- ggplot(data = meta.data, aes(x = total, color = patient, fill = patient)) +
geom_density(alpha = 0.2) +
#scale_fill_manual(values = colpat) +
#scale_color_manual(values = colpat) +
theme_classic() +
scale_x_log10() +
ylab("density") +
geom_vline(xintercept = 100) +
theme(legend.text = element_text(size = 10), legend.title = element_text(size = 10))
pdetectedpat <- ggplot(data = meta.data, aes(x = detected, color = patient, fill = patient)) +
geom_density(alpha = 0.2) +
#scale_fill_manual(values = colpat) +
#scale_color_manual(values = colpat) +
theme_classic() +
scale_x_log10() +
ylab("density") +
geom_vline(xintercept = 100) +
theme(legend.text = element_text(size = 10), legend.title = element_text(size = 10))
# Return the plots as a list
list(ptotalpat, pdetectedpat)## [[1]]
##
## [[2]]
# Extract meta.data from the Seurat object
meta.data <- seuratM@meta.data
# Create the density plot
ptotalpat <- ggplot(data = meta.data, aes(x = total, color = dataset, fill = dataset)) +
geom_density(alpha = 0.2) +
#scale_fill_manual(values = colpat) +
#scale_color_manual(values = colpat) +
theme_classic() +
scale_x_log10() +
ylab("density") +
geom_vline(xintercept = 100) +
theme(legend.text = element_text(size = 10), legend.title = element_text(size = 10))
pdetectedpat <- ggplot(data = meta.data, aes(x = detected, color = dataset, fill = dataset)) +
geom_density(alpha = 0.2) +
#scale_fill_manual(values = colpat) +
#scale_color_manual(values = colpat) +
theme_classic() +
scale_x_log10() +
ylab("density") +
geom_vline(xintercept = 100) +
theme(legend.text = element_text(size = 10), legend.title = element_text(size = 10))
# Return the plots as a list
list(ptotalpat, pdetectedpat)## [[1]]
##
## [[2]]
cell_count <- data.frame(table(seuratM$dataset))
colnames(cell_count) <- c("dataset", "Freq")
hsize <- 1.5
ggplot(cell_count, aes(x = hsize, y = Freq, fill = dataset)) +
#scale_fill_manual(values = colpat2) +
geom_col(color = "white") +
coord_polar(theta = "y") +
xlim(c(0.2, hsize + 0.5)) +
theme_void() +
ggtitle("cell number") +
theme(plot.title = element_text(hjust = 0.5, size = 10), legend.text = element_text(size = 10), legend.title = element_text(size = 10)) +
geom_text(aes(label = Freq), position = position_stack(vjust = 0.5), size = 10)
plot umaps
Idents(seuratM) <- seuratM$RNA_snn_res.0.25
DimPlot(seuratM, reduction = "umap", pt.size = 0.1, raster = FALSE, label = TRUE) 
Idents(seuratM) <- seuratM$RNA_snn_res.0.4
DimPlot(seuratM, reduction = "umap", pt.size = 0.1, raster = FALSE, label = TRUE) 
Idents(seuratM) <- seuratM$RNA_snn_res.0.6
DimPlot(seuratM, reduction = "umap", pt.size = 0.1, raster = FALSE, label = TRUE) 
Idents(seuratM) <- seuratM$patient
DimPlot(seuratM, reduction = "umap", pt.size = 0.1, raster = FALSE) 
DimPlot(seuratM, reduction = "umap", pt.size = 0.1, raster = FALSE) + theme(legend.position = "null")
Idents(seuratM) <- seuratM$clusterName
DimPlot(seuratM, reduction = "umap", pt.size = 0.1, cols = colclusterName, raster = FALSE) 
DimPlot(seuratM, reduction = "umap", pt.size = 0.1, cols = colclusterName, raster = FALSE) + theme(legend.position = "null")
DimPlot(seuratM, reduction = "umap", pt.size = 0.1, cols = colclusterName, raster = FALSE, label = TRUE) + theme(legend.position = "null")
Idents(seuratM) <- seuratM$diseaseCond
DimPlot(seuratM, reduction = "umap", pt.size = 0.1, cols = coldiseaseCond, shuffle = TRUE, raster=FALSE)
DimPlot(seuratM, reduction = "umap", pt.size = 0.1, cols = coldiseaseCond, shuffle = TRUE, raster = FALSE) + theme(legend.position = "null")
Idents(seuratM) <- seuratM$diseaseCond
coldiseaseCond <- c("#dfc27d","lightgrey","lightgrey","lightgrey","lightgrey","lightgrey","lightgrey","lightgrey", "lightgrey", "lightgrey", "lightgrey")
names(coldiseaseCond) <- c("donorheart", "explant", "visit1", "visit2" ,"visit3", "visit4", "visit5", "visitX1", "visitX2", "visitX3", "visitX4")
DimPlot(seuratM, reduction = "umap", pt.size = 0.1, cols = coldiseaseCond, order = "donorheart", raster = FALSE) 
coldiseaseCond <- c("lightgrey","#BE3144","lightgrey","lightgrey","lightgrey","lightgrey","lightgrey","lightgrey", "lightgrey", "lightgrey", "lightgrey")
names(coldiseaseCond) <- c("donorheart", "explant", "visit1", "visit2" ,"visit3", "visit4", "visit5", "visitX1", "visitX2", "visitX3", "visitX4")
DimPlot(seuratM, reduction = "umap", pt.size = 0.1, cols = coldiseaseCond, order = "explant", raster = FALSE) 
coldiseaseCond <- c("lightgrey","lightgrey","#f4a582","lightgrey","lightgrey","lightgrey","lightgrey","lightgrey", "lightgrey","lightgrey", "lightgrey")
names(coldiseaseCond) <- c("donorheart", "explant", "visit1", "visit2" ,"visit3", "visit4", "visit5", "visitX1", "visitX2", "visitX3", "visitX4")
DimPlot(seuratM, reduction = "umap", pt.size = 0.1, cols = coldiseaseCond, order = "visit1", raster = FALSE) 
coldiseaseCond <- c("lightgrey","lightgrey","lightgrey","#B45B5C","lightgrey","lightgrey","lightgrey","lightgrey", "lightgrey", "lightgrey", "lightgrey")
names(coldiseaseCond) <- c("donorheart", "explant", "visit1", "visit2" ,"visit3", "visit4", "visit5", "visitX1", "visitX2", "visitX3", "visitX4")
DimPlot(seuratM, reduction = "umap", pt.size = 0.1, cols = coldiseaseCond, order = "visit2", raster = FALSE)
coldiseaseCond <- c("lightgrey","lightgrey","lightgrey","lightgrey", "#8c510a","lightgrey","lightgrey","lightgrey", "lightgrey", "lightgrey", "lightgrey")
names(coldiseaseCond) <- c("donorheart", "explant", "visit1", "visit2" ,"visit3", "visit4", "visit5", "visitX1", "visitX2", "visitX3", "visitX4")
DimPlot(seuratM, reduction = "umap", pt.size = 0.1, cols = coldiseaseCond, order = "visit3", raster = FALSE)
coldiseaseCond <- c("lightgrey","lightgrey","lightgrey","lightgrey","lightgrey","#202547","lightgrey","lightgrey", "lightgrey","lightgrey", "lightgrey")
names(coldiseaseCond) <- c("donorheart", "explant", "visit1", "visit2" ,"visit3", "visit4", "visit5", "visitX1", "visitX2", "visitX3", "visitX4")
DimPlot(seuratM, reduction = "umap", pt.size = 0.1, cols = coldiseaseCond, order = "visit4", raster = FALSE)
coldiseaseCond <- c("lightgrey","lightgrey","lightgrey","lightgrey","lightgrey","lightgrey","#355C7D","lightgrey", "lightgrey", "lightgrey", "lightgrey")
names(coldiseaseCond) <- c("donorheart", "explant", "visit1", "visit2" ,"visit3", "visit4", "visit5", "visitX1", "visitX2", "visitX3", "visitX4")
DimPlot(seuratM, reduction = "umap", pt.size = 0.1, cols = coldiseaseCond, order = "visit5", raster = FALSE)
coldiseaseCond <- c("lightgrey","lightgrey","lightgrey","lightgrey","lightgrey","lightgrey","lightgrey","#779d8d", "lightgrey", "lightgrey", "lightgrey")
names(coldiseaseCond) <- c("donorheart", "explant", "visit1", "visit2" ,"visit3", "visit4", "visit5", "visitX1", "visitX2", "visitX3", "visitX4")
DimPlot(seuratM, reduction = "umap", pt.size = 0.1, cols = coldiseaseCond, order = "visitX1", raster = FALSE)
coldiseaseCond <- c("lightgrey","lightgrey","lightgrey","lightgrey","lightgrey","lightgrey","lightgrey", "lightgrey", "#01665e", "lightgrey", "lightgrey")
names(coldiseaseCond) <- c("donorheart", "explant", "visit1", "visit2" ,"visit3", "visit4", "visit5", "visitX1", "visitX2", "visitX3", "visitX4")
DimPlot(seuratM, reduction = "umap", pt.size = 0.1, cols = coldiseaseCond, order = "visitX2", raster = FALSE)
coldiseaseCond <- c("lightgrey","lightgrey","lightgrey","lightgrey","lightgrey","lightgrey","lightgrey", "lightgrey", "lightgrey", "#3288BD", "lightgrey")
names(coldiseaseCond) <- c("donorheart", "explant", "visit1", "visit2" ,"visit3", "visit4", "visit5", "visitX1", "visitX2", "visitX3", "visitX4")
DimPlot(seuratM, reduction = "umap", pt.size = 0.1, cols = coldiseaseCond, order = "visitX3", raster = FALSE)
coldiseaseCond <- c("lightgrey","lightgrey","lightgrey","lightgrey","lightgrey","lightgrey","lightgrey", "lightgrey", "lightgrey", "lightgrey", "#BEAED4")
names(coldiseaseCond) <- c("donorheart", "explant", "visit1", "visit2" ,"visit3", "visit4", "visit5", "visitX1", "visitX2", "visitX3", "visitX4")
DimPlot(seuratM, reduction = "umap", pt.size = 0.1, cols = coldiseaseCond, order = "visitX4", raster = FALSE)
##reset coldiseaseCond
coldiseaseCond <- c("#dfc27d","#BE3144","#f4a582","#B45B5C","#8c510a","#202547","#355C7D","#779d8d", "#01665e", "#3288BD", "#BEAED4")
names(coldiseaseCond) <- c("donorheart", "explant", "visit1", "visit2", "visit3", "visit4", "visit5", "visitX1", "visitX2", "visitX3", "visitX4")session info
date()## [1] "Mon Sep 29 19:33:36 2025"sessionInfo()## R version 4.5.1 (2025-06-13)
## Platform: aarch64-apple-darwin20
## Running under: macOS Sequoia 15.1
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.1
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## time zone: Europe/Zurich
## tzcode source: internal
##
## attached base packages:
## [1] grid stats4 stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] NCmisc_1.2.0 VennDiagram_1.7.3 futile.logger_1.4.3
## [4] ggupset_0.4.1 gridExtra_2.3 DOSE_4.2.0
## [7] enrichplot_1.28.4 msigdbr_25.1.1 org.Hs.eg.db_3.21.0
## [10] AnnotationDbi_1.70.0 clusterProfiler_4.16.0 multtest_2.64.0
## [13] metap_1.12 scater_1.35.0 scuttle_1.18.0
## [16] destiny_3.22.0 circlize_0.4.16 muscat_1.22.0
## [19] viridis_0.6.5 viridisLite_0.4.2 lubridate_1.9.4
## [22] forcats_1.0.1 stringr_1.5.2 purrr_1.1.0
## [25] readr_2.1.5 tidyr_1.3.1 tibble_3.3.0
## [28] tidyverse_2.0.0 dplyr_1.1.4 SingleCellExperiment_1.30.1
## [31] SummarizedExperiment_1.38.1 Biobase_2.68.0 GenomicRanges_1.60.0
## [34] GenomeInfoDb_1.44.3 IRanges_2.42.0 S4Vectors_0.46.0
## [37] BiocGenerics_0.54.0 generics_0.1.4 MatrixGenerics_1.20.0
## [40] matrixStats_1.5.0 pheatmap_1.0.13 ggpubr_0.6.1
## [43] ggplot2_4.0.0 Seurat_5.3.0 SeuratObject_5.2.0
## [46] sp_2.2-0
##
## loaded via a namespace (and not attached):
## [1] igraph_2.1.4 ica_1.0-3 plotly_4.11.0
## [4] Formula_1.2-5 tidyselect_1.2.1 bit_4.6.0
## [7] doParallel_1.0.17 clue_0.3-66 lattice_0.22-7
## [10] rjson_0.2.23 blob_1.2.4 S4Arrays_1.8.1
## [13] pbkrtest_0.5.5 parallel_4.5.1 png_0.1-8
## [16] plotrix_3.8-4 cli_3.6.5 ggplotify_0.1.3
## [19] goftest_1.2-3 VIM_6.2.6 variancePartition_1.38.1
## [22] BiocNeighbors_2.2.0 uwot_0.2.3 curl_7.0.0
## [25] mime_0.13 evaluate_1.0.5 tidytree_0.4.6
## [28] ComplexHeatmap_2.24.1 stringi_1.8.7 backports_1.5.0
## [31] lmerTest_3.1-3 qqconf_1.3.2 httpuv_1.6.16
## [34] magrittr_2.0.4 rappdirs_0.3.3 splines_4.5.1
## [37] sctransform_0.4.2 ggbeeswarm_0.7.2 DBI_1.2.3
## [40] jquerylib_0.1.4 smoother_1.3 withr_3.0.2
## [43] corpcor_1.6.10 reformulas_0.4.1 class_7.3-23
## [46] lmtest_0.9-40 formatR_1.14 htmlwidgets_1.6.4
## [49] fs_1.6.6 ggrepel_0.9.6 labeling_0.4.3
## [52] fANCOVA_0.6-1 SparseArray_1.8.1 DESeq2_1.48.2
## [55] ranger_0.17.0 DEoptimR_1.1-4 reticulate_1.43.0
## [58] hexbin_1.28.5 zoo_1.8-14 XVector_0.48.0
## [61] knitr_1.50 ggplot.multistats_1.0.1 UCSC.utils_1.4.0
## [64] RhpcBLASctl_0.23-42 timechange_0.3.0 foreach_1.5.2
## [67] patchwork_1.3.2 caTools_1.18.3 data.table_1.17.8
## [70] ggtree_3.16.3 R.oo_1.27.1 RSpectra_0.16-2
## [73] irlba_2.3.5.1 ggrastr_1.0.2 fastDummies_1.7.5
## [76] gridGraphics_0.5-1 lazyeval_0.2.2 yaml_2.3.10
## [79] survival_3.8-3 scattermore_1.2 crayon_1.5.3
## [82] RcppAnnoy_0.0.22 RColorBrewer_1.1-3 progressr_0.16.0
## [85] later_1.4.4 ggridges_0.5.7 codetools_0.2-20
## [88] GlobalOptions_0.1.2 aod_1.3.3 KEGGREST_1.48.1
## [91] Rtsne_0.17 shape_1.4.6.1 limma_3.64.3
## [94] pkgconfig_2.0.3 TMB_1.9.17 spatstat.univar_3.1-4
## [97] mathjaxr_1.8-0 EnvStats_3.1.0 aplot_0.2.9
## [100] scatterplot3d_0.3-44 spatstat.sparse_3.1-0 ape_5.8-1
## [103] xtable_1.8-4 car_3.1-3 plyr_1.8.9
## [106] httr_1.4.7 rbibutils_2.3 tools_4.5.1
## [109] globals_0.18.0 beeswarm_0.4.0 broom_1.0.10
## [112] nlme_3.1-168 lambda.r_1.2.4 assertthat_0.2.1
## [115] lme4_1.1-37 digest_0.6.37 numDeriv_2016.8-1.1
## [118] Matrix_1.7-4 farver_2.1.2 tzdb_0.5.0
## [121] remaCor_0.0.20 reshape2_1.4.4 yulab.utils_0.2.1
## [124] glue_1.8.0 cachem_1.1.0 polyclip_1.10-7
## [127] Biostrings_2.76.0 mvtnorm_1.3-3 parallelly_1.45.1
## [130] mnormt_2.1.1 statmod_1.5.0 RcppHNSW_0.6.0
## [133] ScaledMatrix_1.16.0 carData_3.0-5 minqa_1.2.8
## [136] pbapply_1.7-4 spam_2.11-1 gson_0.1.0
## [139] gtools_3.9.5 ggsignif_0.6.4 RcppEigen_0.3.4.0.2
## [142] shiny_1.11.1 GenomeInfoDbData_1.2.14 glmmTMB_1.1.12
## [145] R.utils_2.13.0 memoise_2.0.1 rmarkdown_2.29
## [148] scales_1.4.0 R.methodsS3_1.8.2 future_1.67.0
## [151] RANN_2.6.2 Cairo_1.6-5 spatstat.data_3.1-8
## [154] rstudioapi_0.17.1 cluster_2.1.8.1 mutoss_0.1-13
## [157] spatstat.utils_3.2-0 hms_1.1.3 fitdistrplus_1.2-4
## [160] cowplot_1.2.0 colorspace_2.1-2 rlang_1.1.6
## [163] xts_0.14.1 dotCall64_1.2 ggtangle_0.0.7
## [166] laeken_0.5.3 mgcv_1.9-3 xfun_0.53
## [169] e1071_1.7-16 TH.data_1.1-4 iterators_1.0.14
## [172] abind_1.4-8 GOSemSim_2.34.0 treeio_1.32.0
## [175] futile.options_1.0.1 bitops_1.0-9 Rdpack_2.6.4
## [178] promises_1.3.3 RSQLite_2.4.3 qvalue_2.40.0
## [181] sandwich_3.1-1 fgsea_1.34.2 DelayedArray_0.34.1
## [184] proxy_0.4-27 GO.db_3.21.0 compiler_4.5.1
## [187] prettyunits_1.2.0 boot_1.3-32 beachmat_2.24.0
## [190] listenv_0.9.1 Rcpp_1.1.0 edgeR_4.6.3
## [193] BiocSingular_1.24.0 tensor_1.5.1 MASS_7.3-65
## [196] progress_1.2.3 BiocParallel_1.42.2 babelgene_22.9
## [199] spatstat.random_3.4-2 R6_2.6.1 fastmap_1.2.0
## [202] multcomp_1.4-28 fastmatch_1.1-6 rstatix_0.7.2
## [205] vipor_0.4.7 TTR_0.24.4 ROCR_1.0-11
## [208] TFisher_0.2.0 rsvd_1.0.5 vcd_1.4-13
## [211] nnet_7.3-20 gtable_0.3.6 KernSmooth_2.23-26
## [214] miniUI_0.1.2 deldir_2.0-4 htmltools_0.5.8.1
## [217] ggthemes_5.1.0 bit64_4.6.0-1 spatstat.explore_3.5-3
## [220] lifecycle_1.0.4 blme_1.0-6 S7_0.2.0
## [223] nloptr_2.2.1 sass_0.4.10 vctrs_0.6.5
## [226] robustbase_0.99-6 spatstat.geom_3.6-0 sn_2.1.1
## [229] ggfun_0.2.0 future.apply_1.20.0 bslib_0.9.0
## [232] pillar_1.11.1 gplots_3.2.0 pcaMethods_2.0.0
## [235] locfit_1.5-9.12 jsonlite_2.0.0 GetoptLong_1.0.5